The pulse as a biometric measure of well-being

For decades, economists avoided the study of subjective well-being (SWB) and rejected efforts to understand its correlates or to use measures of well-being in economic analysis. This changed when, in 1978, Richard Freeman showed that job satisfaction was a good predictor of quits (Freeman 1978). For the first time, economists have looked at the potential that SWB may have in predicting economic behavior. The SWB has since been treated as a means of measuring the utility of individuals (Frey and Stutzer 2002) and has even been identified as a major public policy objective (Layard 2005).

But the value of SWB data has been questioned recently by economists who argue that key empirical regularities in the welfare literature cannot be replicated using nonparametric identification techniques due to assumptions about the underlying functional form of ordered responses that are typically elicited in survey questions. on SWB (Bond and Lang 2019). This position has in turn been contested by Chen et al. (2021), who argue that Bond and Lang’s critique does not hold up if one focuses on ranking median versus average happiness.

Yet SWB measures have been causally linked to longevity (Diener and Chan 2011), wound healing (Christian et al. 2006), and improved cardiovascular health (see De Neve et al. 2013 for a review journal), providing some validation of SWB as a metric capturing individual well-being. Additionally, the way unhappiness increases and then decreases with age follows the same bump shape in antidepressant drugs, providing additional validation from the SWB (Blanchflower and Oswald 2016, Blanchflower and Graham 2021, Blanchflower and Bryson 2021a).

While there is clearly some value in SWB metrics, the review by Bond and Lang (2019) means that it is useful to identify other easy-to-measure ways to establish people’s well-being. Another reason for doing this is that SWB’s self-assessments can be unreliable. For example, Johnston et al. (2009) find no evidence of an income / health gradient using self-reported hypertension as a measure of well-being, but a large gradient when using objectively measured hypertension. The authors conclude that self-reported health measures may underestimate true income-related health inequalities.

In a new study (Blanchflower and Bryson 2021b), we introduce pulse rate as a biometric indicator of well-being that has been largely overlooked in the literature. Like SWB, the pulse is relatively easy to measure, but it has the advantage of being a biomarker with a cardinal scale.

Using the England and Scotland Health Surveys and the National Child Development Study (NCDS), which tracks all people born in Britain in a single week in 1958, we first examine whether the equations of pulse rates look like the SWB equations. It turns out that they have similar correlates: pulse rates are higher in women, single people, widowers, the unemployed and disabled, the less educated, those with a higher body mass index (BMI), smokers and drinkers, and those with a low body mass index (BMI). Income. Pulse rates also vary by area, being lowest in prosperous areas and highest in deprived areas. The fact that a pulse equation closely resembles a General Health Questionnaire (GHQ) equation helps validate SWB measurements. However, unlike mental and general health, the pulse rate monotonically decreases with age.

We then show that the pulse is strongly correlated with the SWB in cross section having controlled the personal characteristics. It is positively correlated with the GHQ36 dissatisfaction scores with an r-squared of 0.66 (see Figure 1). It is negatively correlated with self-rated general health in the English and Scottish health surveys. It is also negatively associated with life satisfaction and the WEMWBS Wellbeing Scale in the Scottish Health Survey.

Figure 1 Health Survey for England CHQ36 and pulse rate, 1998-2018

Remarks: n = 109,000. The mean of the GHQ36 dissatisfaction scale is 10.7. The average heart rate is 70.22

In longitudinal data from the NCDS, we find that the heart rate collected from 9,000 NCDS cohort members in their 40s (42 years) is predictive of SWB, general health, employment, and optimism about in the future a decade or more later, even controlling for lagged dependent variables, health-related behaviors, and other biomarkers such as BMI.

Today, advancements in smart device technology mean it’s cheap and easy to measure your pulse (Gyrard & Sheth 2020). This is common practice in some contexts – for example, golfers on the PGA Tour have been doing this for some time. So it seems a good idea for healthcare professionals and academics to pay more attention to this data, and perhaps individuals to pay more attention to their pulse, alongside other biomarkers such as blood pressure and heart rate. BMI. Pulse rates could also be used by academics as a plausible instrument for the SWB in efforts to draw causal inferences about the SWB on economic and social outcomes. Heart rate appears to be an objective way to measure well-being.

The references

Blanchflower, DG and A Bryson (2021a), “Biden, Covid, and mental health in America”, NBER Working Paper # 29040.

Blanchflower, DG and A Bryson (2021b), “Taking the Pulse of Nations: A Biometric Measure of Well-Being”, NBER Working Paper # 29587

Blanchflower, DG and C Graham (2021), “The Decline in Mid-Life Well-Being: A Critique,” ​​Social Indicators Research.

Blanchflower, DG and AJ Oswald (2016), “Antidepressants and Aging: A New Form of U-Shaped Wellness Evidence Across Life”, Journal of Economic Behavior and Organization 127: 46-58.

Bond, TN and K Lang (2019), “The Sad Truth About Happiness Weighs in”, Political economy journal 127 (4): 1629-1640.

Chen, LY, E Oparina, N Powdthavee, and S Srisuma (2021), “Robust Ranking of Happiness Outcomes: A Median Regression Perspective”.

Christian, LM, JE Graham, DA Padgett, R Glaser and JK Kiecolt-Glaser (2006), “Stress and wound healing”, Neuroimmunomodulation 13: 337-346.

De Neve, JE, E Diener, L Tay and C Xuereb (2013), “The objective benefits of subjective well-being”, in J Helliwell, R Layard and J Sachs (eds), World Happiness Report 2013, United Nations Sustainable Development Solutions Network.

Diener, E and M Chan (2011), “Happy people live longer: subjective well-being contributes to health and longevity”, Applied psychology: health and well-being 3: 1-43.

Freeman, RB (1978), “Job satisfaction as an economic variable”, AAmerican Economic Review 68 (2): 135-41

Frey, BS and A Stutzer (2002), “What Can Economists Learn from Happiness Research? “, Economic literature review 40 (2): 402–35.

Johnston, DW, C Propper and MA Shields (2009), “Comparing subjective and objective devices of health: evidence from hypertension for the Income / Health gradient”, Journal of Health Economics 28: 540-552.

Gyrard, A and A Sheth (2020), “IAMHAPPY: Towards an IoT Knowledge-Based Cross-Domain Wellness Recommendation System for Everyday Happiness”, Smart health 15: 100083.

Layard, R (2005), Happiness: lessons from a new science, Penguin.